Differential evolution-based parameters optimisation and feature selection for support vector machine

2016 ◽  
Vol 13 (4) ◽  
pp. 355 ◽  
Author(s):  
Jun Li ◽  
Lixin Ding ◽  
Bo Li
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Differential Evolution ◽  
Support Vector ◽  
Selection For

Feature selection for support vector machine in financial crisis prediction: a case study in China

2010 ◽  
Vol 27 (4) ◽  
pp. 299-310 ◽  
Author(s):  
Xinping Song ◽  
Yongsheng Ding ◽  
Jingwen Huang ◽  
Yan Ge
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Financial Crisis ◽  
Support Vector ◽  
Selection For

scholarly journals Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jhansi Rani Kaka ◽  
K. Satya Prasad
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Differential Evolution ◽  
Optimization Method ◽  
Support Vector ◽  
Local Optima ◽  
Whale Optimization ◽  
Multiclass Support Vector Machine ◽  
Poor Convergence

Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.

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